{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison of clustering of node embeddings with a traditional community detection method\n",
    "## Use case based on terrorist attack data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this use case is to demonstrate how node embeddings from graph convolutional neural networks trained in unsupervised manner are comparable to standard community detection methods based on graph partitioning. \n",
    "\n",
    "Specifically, we use unsupervised [graphSAGE](http://snap.stanford.edu/graphsage/) approach to learn node embeddings of terrorist groups in a publicly available dataset of global terrorism events, and analyse clusters of these embeddings. We compare the clusters to communities produced by [Infomap](http://www.mapequation.org), a state-of-the-art approach of graph partitioning based on information theory. \n",
    "\n",
    "We argue that clustering based on unsupervised graphSAGE node embeddings allow for richer representation of the data than its graph partitioning counterpart as the former takes into account node features together with the graph structure, while the latter utilises only the graph structure. \n",
    "\n",
    "We demonstrate, using the terrorist group dataset, that the infomap communities and the graphSAGE embedding clusters (GSEC) provide qualitatively different insights into underlying data patterns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__The Global Terrorism Database (GTD)__  used in this demo is available here: https://www.kaggle.com/START-UMD/gtd. GTD is an open-source database including information on terrorist attacks around the world from 1970 through 2017. The GTD includes systematic data on domestic as well as international terrorist incidents that have occurred during this time period and now includes more than 180,000 attacks. The database is maintained by researchers at the National Consortium for the Study of Terrorism and Responses to Terrorism (START), headquartered at the University of Maryland. For information refer to the initial data source: https://www.start.umd.edu/gtd/.\n",
    "\n",
    "Full dataset contains information on more than 180,000 Terrorist Attacks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Glossary:\n",
    "For this particular study we adopt the following terminology:\n",
    "\n",
    "- __a community__ is a group of nodes produced by a traditional community detection algorithm (infomap community in this use case)\n",
    "- __a cluster__ is a group of nodes that were clustered together using a clustering algorithm applied to node embeddings (here, [DBSCAN clustering](https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf) applied to unsupervised GraphSAGE embeddings). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more detailed explanation of unsupervised graphSAGE see [Unsupervised graphSAGE demo](https://github.com/stellargraph/stellargraph/master/demos/embeddings/embeddings-unsupervised-graphsage-cora.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rest of the demo is structured as follows. First, we load the data and pre-process it (see `utils.py` for detailed steps of network and features generation). Then we apply infomap and visualise results of one selected community obtained from this method. Next, we apply unsupervised graphSAGE on the same data and extract node embeddings. We first tune DBSCAN hyperparameters, and apply DBSCAN that produces sufficient numbers of clusters with minimal number of noise points. We look at the resulting clusters, and investigate a single selected cluster in terms of the graph structure and features of the nodes in this cluster. Finally, we conclude our investigation with the summary of the results.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import igraph as ig\n",
    "\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "import random\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') # supress warnings due to some future deprications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import stellargraph as sg\n",
    "from stellargraph.data import EdgeSplitter\n",
    "from stellargraph.mapper import GraphSAGELinkGenerator, GraphSAGENodeGenerator\n",
    "from stellargraph.layer import GraphSAGE, link_classification\n",
    "from stellargraph.data import UniformRandomWalk\n",
    "from stellargraph.data import UnsupervisedSampler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from tensorflow import keras\n",
    "\n",
    "from stellargraph import globalvar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mplleaflet\n",
    "from itertools import count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the raw data of terrorist attacks that we use as a starting point of our analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_raw = pd.read_csv(\"~/data/globalterrorismdb_0718dist.csv\", sep=',', engine='python', encoding = \"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading pre-processed features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting feature set is the aggregation of features for each of the terrorist groups (`gname`). We collect such features as total number of attacks per terrorist group, number of perpetrators etc. We use `targettype` and `attacktype` (number of attacks/targets of particular type) and transform it to a wide representation of data (e.g each type is a separate column with the counts for each group). Refer to `utils.py` for more detailed steps of the pre-processing and data cleaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gnames_features = utils.load_features(input_data=dt_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Bombing/Explosion</th>\n",
       "      <th>Facility/Infrastructure Attack</th>\n",
       "      <th>...</th>\n",
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       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14th of December Command</td>\n",
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      ],
      "text/plain": [
       "                                 gname  n_attacks  n_nperp  n_nkil  n_nwound  \\\n",
       "0                                1 May         10      3.0     2.0       0.0   \n",
       "1                           14 K Triad          4     24.0     0.0       0.0   \n",
       "2                   14 March Coalition          1      0.0     5.0      80.0   \n",
       "3             14th of December Command          3      0.0     0.0       0.0   \n",
       "4  15th of September Liberation Legion          1      9.0     0.0       1.0   \n",
       "\n",
       "   success_ratio  Armed Assault  Assassination  Bombing/Explosion  \\\n",
       "0            0.9            0.0            4.0                6.0   \n",
       "1            1.0            0.0            0.0                4.0   \n",
       "2            1.0            1.0            0.0                0.0   \n",
       "3            1.0            0.0            0.0                3.0   \n",
       "4            1.0            0.0            0.0                1.0   \n",
       "\n",
       "   Facility/Infrastructure Attack           ...             Other  Police  \\\n",
       "0                             0.0           ...               0.0     1.0   \n",
       "1                             0.0           ...               0.0     0.0   \n",
       "2                             0.0           ...               0.0     0.0   \n",
       "3                             0.0           ...               0.0     0.0   \n",
       "4                             0.0           ...               0.0     0.0   \n",
       "\n",
       "   Private Citizens & Property  Religious Figures/Institutions  \\\n",
       "0                          0.0                             0.0   \n",
       "1                          3.0                             0.0   \n",
       "2                          0.0                             0.0   \n",
       "3                          0.0                             2.0   \n",
       "4                          0.0                             0.0   \n",
       "\n",
       "   Telecommunication  Terrorists/Non-State Militia  Tourists  Transportation  \\\n",
       "0                0.0                           0.0       0.0             0.0   \n",
       "1                0.0                           0.0       0.0             0.0   \n",
       "2                0.0                           1.0       0.0             0.0   \n",
       "3                0.0                           0.0       0.0             0.0   \n",
       "4                0.0                           0.0       0.0             0.0   \n",
       "\n",
       "   Utilities  Violent Political Party  \n",
       "0        0.0                      0.0  \n",
       "1        0.0                      0.0  \n",
       "2        0.0                      0.0  \n",
       "3        0.0                      0.0  \n",
       "4        0.0                      0.0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnames_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Edgelist creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The raw dataset contains information for the terrotist attacks. It contains information about some incidents being related. However, the resulting graph is very sparse. Therefore, we create our own schema for the graph, where two terrorist groups are connected, if they had at least one terrorist attack in the same country and in the same decade. To this end we proceed as follows:\n",
    "\n",
    "-  we group the event data  by `gname` - the names of the terrorist organisations.\n",
    "-  we create a new feature `decade` based on `iyear`, where one bin consists of 10 years (attacks of 70s, 80s, and so on). \n",
    "-  we add the concatination of of the decade and the country of the attack: `country_decade` which will become a link between two terrorist groups.\n",
    "-  finally, we create an edgelist, where two terrorist groups are linked if they have operated in the same country in the same decade. Edges are undirected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, some edges are created based on the column `related` in the raw event data. The description of the data does not go into details how these incidents were related.  We utilise this information creating a link for terrorist groups if the terrorist attacks performed by these groups were related. If related events corresponded to the same terrorist group, they were discarded (we don't use self-loops in the graph). However, the majority of such links are already covered by `country_decade` edge type. Refer to `utils.py` for more information on graph creation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = utils.load_network(input_data=dt_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 3490\n",
      "Number of edges: 106903\n",
      "Average degree:  61.2625\n"
     ]
    }
   ],
   "source": [
    "print(nx.info(G))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connected components of the network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the graph is disconnected, consisting of 21 connected components. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n"
     ]
    }
   ],
   "source": [
    "print(nx.number_connected_components(G))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the sizes of the connected components:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3435, 7, 6, 5, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n"
     ]
    }
   ],
   "source": [
    "Gcc = sorted(nx.connected_component_subgraphs(G), key=len, reverse=True)\n",
    "cc_sizes = []\n",
    "for cc in list(Gcc):\n",
    "    cc_sizes.append(len(cc.nodes()))\n",
    "print(cc_sizes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of connected components' sizes shows that there is a single large component, and a few isolated groups. We expect the community detection/node embedding clustering algorithms discussed below to discover non-trivial communities that are not simply the connected components of the graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Traditional community detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We perform traditional community detection via `infomap` implemented in `igraph`. We translate the original `networkx` graph object to `igraph`, apply `infomap` to it to detect communities, and assign the resulting community memberships back to the `networkx` graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'IGRAPH U--- 3490 106903 -- '"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# translate the object into igraph\n",
    "g_ig = ig.Graph.Adjacency((nx.to_numpy_matrix(G) > 0).tolist(), mode=ig.ADJ_UNDIRECTED) # convert via adjacency matrix\n",
    "g_ig.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clustering with 3490 elements and 160 clusters\n"
     ]
    }
   ],
   "source": [
    "# perform community detection\n",
    "random.seed(123)\n",
    "c_infomap = g_ig.community_infomap()\n",
    "print(c_infomap.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get 160 communities, meaning that the largest connected components of the graph are partitioned into more granular groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 160 artists>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the community sizes\n",
    "infomap_sizes = c_infomap.sizes()\n",
    "plt.title(\"Infomap community sizes\")\n",
    "plt.xlabel('community id')\n",
    "plt.ylabel('number of nodes')\n",
    "plt.bar(list(range(1, len(infomap_sizes)+1)), infomap_sizes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6287979581321445"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modularity metric for infomap\n",
    "c_infomap.modularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The discovered infomap communities have smooth distribution of cluster sizes, which indicates that the underlying graph structure has a natural partitioning. The modularity score is also pretty high indicating that nodes are more tightly connected within clusters than expected from random graph, i.e., the discovered communities are tightly-knit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assign community membership results back to networkx, keep the dictionary for later comparisons with the clustering\n",
    "infomap_com_dict = dict(zip(list(G.nodes()), c_infomap.membership))\n",
    "nx.set_node_attributes(G, infomap_com_dict, 'c_infomap')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualisation of the infomap communities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualise the resulting communities using maps as the constructed network is based partially on the geolocation. The raw data have a lat-lon coordinates for each of the attacks. Terrorist groups might perform attacks in different locations. However, currently the simplified assumption is made, and the average of latitude and longitude for each terrorist group is calculated. Note that it might result in some positions being \"off\", but it is good enough to provide a glimpse whether the communities are consistent with the location of that groups. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3490, 2)\n",
      "3490\n"
     ]
    }
   ],
   "source": [
    "# fill NA based on the country name\n",
    "dt_raw.latitude[dt_raw['gname']=='19th of July Christian Resistance Brigade'] = 12.136389 \n",
    "dt_raw.longitude[dt_raw['gname']=='19th of July Christian Resistance Brigade'] = -86.251389\n",
    "\n",
    "# filter only groups that are present in a graph\n",
    "dt_filtered = dt_raw[dt_raw['gname'].isin(list(G.nodes()))]\n",
    "\n",
    "# collect averages of latitude and longitude for each of gnames\n",
    "avg_coords = dt_filtered.groupby('gname')[['latitude', 'longitude']].mean()\n",
    "\n",
    "print(avg_coords.shape)\n",
    "print(len(G.nodes()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As plotting the whole graph is not feasible, we investigate a single community"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Specify community id in the range of infomap total number of clusters `len(infomap_sizes)`__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "com_id = 50 # smaller number - larger community, as it's sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 18\n",
      "Number of edges: 103\n",
      "Average degree:  11.4444\n"
     ]
    }
   ],
   "source": [
    "# extraction of a subgraph from the nodes in this community\n",
    "com_G = G.subgraph([n for n, attrdict in G.nodes.items() if attrdict['c_infomap'] == com_id])\n",
    "print(nx.info(com_G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FlaIoiqIoSp1QYaUoiqIoilInVFgpiqIoiqLUCRVWiqIoiqIodUKFlaIoiqIoSp1QYaUoiqIoilInVFgpiqIoiqLUCRVWiqIoiqIodUKFlaIoiqIoSp1QYaUoiqIoilInVFgpiqIoiqLUCRVWiqIoiqIodUKFlaIoiqIoSp1QYaUoiqIoilInVFgpiqIoiqLUCRVWiqIOd3htAAAAz0lEQVQoiqIodUKFlaIoiqIoSp1QYaUoiqIoilInVFgpiqIoiqLUCRVWiqIoiqIodUKFlaIoiqIoSp1QYaUoiqIoilInVFgpiqIoiqLUCRVWiqIoiqIodUKFlaIoiqIoSp1QYaUoiqIoilInVFgpiqIoiqLUCRVWiqIoiqIodUKFlaIoiqIoSp1QYaUoiqIoilInVFgpiqIoiqLUCRVWiqIoiqIodUKFlaIoiqIoSp1QYaUoiqIoilInVFgpiqIoiqLUCRVWiqIoiqIodeL/BxTFleeCfkezAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot community structure only\n",
    "pos = nx.random_layout(com_G, seed=123)\n",
    "plt.figure(figsize=(10, 8))\n",
    "nx.draw_networkx(com_G, pos, edge_color='#26282b', node_color='blue', alpha=0.3)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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  "http://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",
  {maxZoom:19, attribution: '<a href="https://github.com/jwass/mplleaflet">mplleaflet</a> | Map data (c) <a href="http://openstreetmap.org">OpenStreetMap</a> contributors'}).addTo(map);
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if (gjData.features.length != 0) {
  var gj = L.geoJson(gjData, {
    style: function (feature) {
      return feature.properties;
    },
    pointToLayer: function (feature, latlng) {
      var icon = L.divIcon({'html': feature.properties.html, 
        iconAnchor: [feature.properties.anchor_x, 
                     feature.properties.anchor_y], 
          className: 'empty'});  // What can I do about empty?
      return L.marker(latlng, {icon: icon});
    }
  });
  gj.addTo(map);
  
  map.fitBounds(gj.getBounds());
} else {
  map.setView([0, 0], 1);
}
</script>
</body>\" width=\"100%\" height=\"360\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot on the map\n",
    "nodes = com_G.nodes()\n",
    "com_avg_coords = avg_coords[avg_coords.index.isin(list(nodes))]\n",
    "com_avg_coords.fillna(com_avg_coords.mean(), inplace=True) # fill missing values with the average\n",
    "new_order = [1,0]\n",
    "com_avg_coords = com_avg_coords[com_avg_coords.columns[new_order]]\n",
    "pos = com_avg_coords.T.to_dict('list')  # layout is based on the provided coordindates\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "nx.draw_networkx_edges(com_G, pos, edge_color='grey')\n",
    "nx.draw_networkx_nodes(com_G, pos, nodelist=nodes, \n",
    "                            with_labels=True, node_size=200, alpha=0.5)\n",
    "nx.draw_networkx_labels(com_G, pos, font_color='#362626', font_size=50)\n",
    "mplleaflet.display(fig=ax.figure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(**N.B.:** the above interactive plot will only appear after running the cell, and is not rendered in Github!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of results based on infomap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Infomap is a robust community detection algorithm, and shows good results that are in line with the expectations. Most of the communities are tightly connected and reflect the geographical position of the events of the terrorist groups. That is because the graph schema is expressed as \n",
    "> _two terrorist groups are connected if they have terrorist events in the same country in the same decade_. \n",
    "\n",
    "However, no node features are taken into account in the case of the traditional community detection.\n",
    "\n",
    "Next, we explore the GSEC approach, where node features are used along with the graph structure. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node represenatation learning with unsupervised graphSAGE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we apply unsupervised GraphSAGE that takes into account node features as well as graph structure, to produce *node embeddings*. In our case, similarity of node embeddings depicts similarity of the terrorist groups in terms of their operations, targets and attack types (node features) as well as in terms of time and place of attacks (graph structure)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3490, 34)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we reload the graph to get rid of assigned attributes\n",
    "G = utils.load_network(input_data=dt_raw) # to make sure that graph is clean\n",
    "\n",
    "# filter features to contain only gnames that are among nodes of the network\n",
    "filtered_features = gnames_features[gnames_features['gname'].isin(list(G.nodes()))]\n",
    "filtered_features.set_index('gname', inplace=True)\n",
    "filtered_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_attacks</th>\n",
       "      <th>n_nperp</th>\n",
       "      <th>n_nkil</th>\n",
       "      <th>n_nwound</th>\n",
       "      <th>success_ratio</th>\n",
       "      <th>Armed Assault</th>\n",
       "      <th>Assassination</th>\n",
       "      <th>Bombing/Explosion</th>\n",
       "      <th>Facility/Infrastructure Attack</th>\n",
       "      <th>Hijacking</th>\n",
       "      <th>...</th>\n",
       "      <th>Other</th>\n",
       "      <th>Police</th>\n",
       "      <th>Private Citizens &amp; Property</th>\n",
       "      <th>Religious Figures/Institutions</th>\n",
       "      <th>Telecommunication</th>\n",
       "      <th>Terrorists/Non-State Militia</th>\n",
       "      <th>Tourists</th>\n",
       "      <th>Transportation</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>Violent Political Party</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gname</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 May</th>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14 March Coalition</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14th of December Command</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15th of September Liberation Legion</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16 January Organization for the Liberation of Tripoli</th>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    n_attacks  n_nperp  \\\n",
       "gname                                                                    \n",
       "1 May                                                      10      3.0   \n",
       "14 March Coalition                                          1      0.0   \n",
       "14th of December Command                                    3      0.0   \n",
       "15th of September Liberation Legion                         1      9.0   \n",
       "16 January Organization for the Liberation of T...         24      0.0   \n",
       "\n",
       "                                                    n_nkil  n_nwound  \\\n",
       "gname                                                                  \n",
       "1 May                                                  2.0       0.0   \n",
       "14 March Coalition                                     5.0      80.0   \n",
       "14th of December Command                               0.0       0.0   \n",
       "15th of September Liberation Legion                    0.0       1.0   \n",
       "16 January Organization for the Liberation of T...     1.0      32.0   \n",
       "\n",
       "                                                    success_ratio  \\\n",
       "gname                                                               \n",
       "1 May                                                         0.9   \n",
       "14 March Coalition                                            1.0   \n",
       "14th of December Command                                      1.0   \n",
       "15th of September Liberation Legion                           1.0   \n",
       "16 January Organization for the Liberation of T...            1.0   \n",
       "\n",
       "                                                    Armed Assault  \\\n",
       "gname                                                               \n",
       "1 May                                                         0.0   \n",
       "14 March Coalition                                            1.0   \n",
       "14th of December Command                                      0.0   \n",
       "15th of September Liberation Legion                           0.0   \n",
       "16 January Organization for the Liberation of T...           20.0   \n",
       "\n",
       "                                                    Assassination  \\\n",
       "gname                                                               \n",
       "1 May                                                         4.0   \n",
       "14 March Coalition                                            0.0   \n",
       "14th of December Command                                      0.0   \n",
       "15th of September Liberation Legion                           0.0   \n",
       "16 January Organization for the Liberation of T...            0.0   \n",
       "\n",
       "                                                    Bombing/Explosion  \\\n",
       "gname                                                                   \n",
       "1 May                                                             6.0   \n",
       "14 March Coalition                                                0.0   \n",
       "14th of December Command                                          3.0   \n",
       "15th of September Liberation Legion                               1.0   \n",
       "16 January Organization for the Liberation of T...                4.0   \n",
       "\n",
       "                                                    Facility/Infrastructure Attack  \\\n",
       "gname                                                                                \n",
       "1 May                                                                          0.0   \n",
       "14 March Coalition                                                             0.0   \n",
       "14th of December Command                                                       0.0   \n",
       "15th of September Liberation Legion                                            0.0   \n",
       "16 January Organization for the Liberation of T...                             0.0   \n",
       "\n",
       "                                                    Hijacking  \\\n",
       "gname                                                           \n",
       "1 May                                                     0.0   \n",
       "14 March Coalition                                        0.0   \n",
       "14th of December Command                                  0.0   \n",
       "15th of September Liberation Legion                       0.0   \n",
       "16 January Organization for the Liberation of T...        0.0   \n",
       "\n",
       "                                                             ...             \\\n",
       "gname                                                        ...              \n",
       "1 May                                                        ...              \n",
       "14 March Coalition                                           ...              \n",
       "14th of December Command                                     ...              \n",
       "15th of September Liberation Legion                          ...              \n",
       "16 January Organization for the Liberation of T...           ...              \n",
       "\n",
       "                                                    Other  Police  \\\n",
       "gname                                                               \n",
       "1 May                                                 0.0     1.0   \n",
       "14 March Coalition                                    0.0     0.0   \n",
       "14th of December Command                              0.0     0.0   \n",
       "15th of September Liberation Legion                   0.0     0.0   \n",
       "16 January Organization for the Liberation of T...    0.0     0.0   \n",
       "\n",
       "                                                    Private Citizens & Property  \\\n",
       "gname                                                                             \n",
       "1 May                                                                       0.0   \n",
       "14 March Coalition                                                          0.0   \n",
       "14th of December Command                                                    0.0   \n",
       "15th of September Liberation Legion                                         0.0   \n",
       "16 January Organization for the Liberation of T...                          0.0   \n",
       "\n",
       "                                                    Religious Figures/Institutions  \\\n",
       "gname                                                                                \n",
       "1 May                                                                          0.0   \n",
       "14 March Coalition                                                             0.0   \n",
       "14th of December Command                                                       2.0   \n",
       "15th of September Liberation Legion                                            0.0   \n",
       "16 January Organization for the Liberation of T...                             0.0   \n",
       "\n",
       "                                                    Telecommunication  \\\n",
       "gname                                                                   \n",
       "1 May                                                             0.0   \n",
       "14 March Coalition                                                0.0   \n",
       "14th of December Command                                          0.0   \n",
       "15th of September Liberation Legion                               0.0   \n",
       "16 January Organization for the Liberation of T...                0.0   \n",
       "\n",
       "                                                    Terrorists/Non-State Militia  \\\n",
       "gname                                                                              \n",
       "1 May                                                                        0.0   \n",
       "14 March Coalition                                                           1.0   \n",
       "14th of December Command                                                     0.0   \n",
       "15th of September Liberation Legion                                          0.0   \n",
       "16 January Organization for the Liberation of T...                           0.0   \n",
       "\n",
       "                                                    Tourists  Transportation  \\\n",
       "gname                                                                          \n",
       "1 May                                                    0.0             0.0   \n",
       "14 March Coalition                                       0.0             0.0   \n",
       "14th of December Command                                 0.0             0.0   \n",
       "15th of September Liberation Legion                      0.0             0.0   \n",
       "16 January Organization for the Liberation of T...       0.0             0.0   \n",
       "\n",
       "                                                    Utilities  \\\n",
       "gname                                                           \n",
       "1 May                                                     0.0   \n",
       "14 March Coalition                                        0.0   \n",
       "14th of December Command                                  0.0   \n",
       "15th of September Liberation Legion                       0.0   \n",
       "16 January Organization for the Liberation of T...        0.0   \n",
       "\n",
       "                                                    Violent Political Party  \n",
       "gname                                                                        \n",
       "1 May                                                                   0.0  \n",
       "14 March Coalition                                                      0.0  \n",
       "14th of December Command                                                0.0  \n",
       "15th of September Liberation Legion                                     0.0  \n",
       "16 January Organization for the Liberation of T...                      0.0  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_features.head() # take a glimpse at the feature data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We perform a log-transform of the feature set to rescale feature values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transforming features to be on log scale\n",
    "node_features = filtered_features.transform(lambda x: np.log1p(x)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sanity check that there are no misspelled gnames left\n",
    "set(list(G.nodes())) - set(list(node_features.index.values)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsupervised graphSAGE\n",
    "\n",
    "(For a detailed unsupervised GraphSAGE workflow with a narrative, see [Unsupervised graphSAGE demo](https://github.com/stellargraph/stellargraph/master/demos/embeddings/embeddings-unsupervised-graphsage-cora.ipynb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StellarGraph: Undirected multigraph\n",
      " Nodes: 3490, Edges: 106903\n",
      "\n",
      " Node types:\n",
      "  default: [3490]\n",
      "    Edge types: default-default->default\n",
      "\n",
      " Edge types:\n",
      "    default-default->default: [106903]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Gs = sg.StellarGraph(G, node_features=node_features)\n",
    "print(Gs.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameter specification\n",
    "number_of_walks = 3\n",
    "length = 5\n",
    "batch_size = 50\n",
    "epochs = 10\n",
    "num_samples = [20, 20]\n",
    "layer_sizes = [100, 100]\n",
    "learning_rate = 1e-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "unsupervisedSamples = UnsupervisedSampler(Gs, nodes=G.nodes(), length=length, number_of_walks=number_of_walks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running GraphSAGELinkGenerator with an estimated 2094 batches generated on the fly per epoch.\n"
     ]
    }
   ],
   "source": [
    "train_gen = GraphSAGELinkGenerator(Gs, batch_size, num_samples).flow(unsupervisedSamples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert len(layer_sizes) == len(num_samples)\n",
    "\n",
    "graphsage = GraphSAGE(\n",
    "        layer_sizes=layer_sizes, generator=train_gen, bias=True, dropout=0.0, normalize=\"l2\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now build a Keras model from the GraphSAGE class that we can use for unsupervised predictions. We add a `link_classfication` layer as unsupervised training operates on node pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "link_classification: using 'ip' method to combine node embeddings into edge embeddings\n"
     ]
    }
   ],
   "source": [
    "x_inp, x_out = graphsage.build()\n",
    "\n",
    "prediction = link_classification(\n",
    "        output_dim=1, output_act=\"sigmoid\", edge_embedding_method='ip'\n",
    "    )(x_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
    "\n",
    "model.compile(\n",
    "        optimizer=keras.optimizers.Adam(lr=learning_rate),\n",
    "        loss=keras.losses.binary_crossentropy,\n",
    "        metrics=[keras.metrics.binary_accuracy],\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      " - 201s - loss: 0.5907 - binary_accuracy: 0.7026\n",
      "Epoch 2/10\n",
      " - 192s - loss: 0.5794 - binary_accuracy: 0.7291\n",
      "Epoch 3/10\n",
      " - 188s - loss: 0.5792 - binary_accuracy: 0.7343\n",
      "Epoch 4/10\n",
      " - 194s - loss: 0.5770 - binary_accuracy: 0.7352\n",
      "Epoch 5/10\n",
      " - 188s - loss: 0.5751 - binary_accuracy: 0.7387\n",
      "Epoch 6/10\n",
      " - 180s - loss: 0.5752 - binary_accuracy: 0.7380\n",
      "Epoch 7/10\n",
      " - 190s - loss: 0.5747 - binary_accuracy: 0.7372\n",
      "Epoch 8/10\n",
      " - 192s - loss: 0.5730 - binary_accuracy: 0.7360\n",
      "Epoch 9/10\n",
      " - 195s - loss: 0.5724 - binary_accuracy: 0.7409\n",
      "Epoch 10/10\n",
      " - 194s - loss: 0.5724 - binary_accuracy: 0.7407\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "        train_gen,\n",
    "        epochs=epochs,\n",
    "        verbose=2,\n",
    "        use_multiprocessing=False,\n",
    "        workers=1,\n",
    "        shuffle=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting node embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_ids = list(Gs.nodes())\n",
    "node_gen = GraphSAGENodeGenerator(Gs, batch_size, num_samples).flow(node_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_model = keras.Model(inputs=x_inp[::2], outputs=x_out[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70/70 [==============================] - 3s 41ms/step\n"
     ]
    }
   ],
   "source": [
    "node_embeddings = embedding_model.predict_generator(node_gen, workers=4, verbose=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2D t-sne plot of the resulting node embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we visually check whether embeddings have some underlying cluster structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_embeddings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TSNE visualisation to check whether the embeddings have some structure:\n",
    "X = node_embeddings\n",
    "if X.shape[1] > 2:\n",
    "    transform = TSNE #PCA \n",
    "\n",
    "    trans = transform(n_components=2, random_state=123)\n",
    "    emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_ids)\n",
    "else:\n",
    "    emb_transformed = pd.DataFrame(X, index=node_ids)\n",
    "    emb_transformed = emb_transformed.rename(columns = {'0':0, '1':1})\n",
    "\n",
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7,7))\n",
    "ax.scatter(emb_transformed[0], emb_transformed[1], alpha=alpha)\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title('{} visualization of GraphSAGE embeddings'.format(transform.__name__))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### t-sne colored by infomap\n",
    "We also depict the same t-sne plot colored by infomap communities. As we can observe t-sne of GraphSAGE embeddings do not really separate the infomap communities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 't-sne with colors corresponding to infomap communities')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "emb_transformed['infomap_clusters'] = emb_transformed.index.map(infomap_com_dict)\n",
    "plt.scatter(emb_transformed[0], emb_transformed[1], c=emb_transformed['infomap_clusters'], \n",
    "            cmap='Spectral', edgecolors='black', alpha=0.3, s=100) \n",
    "plt.title(\"t-sne with colors corresponding to infomap communities\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we apply dbscan algorithm to cluster the embeddings. dbscan has two hyperparameters: `eps` and `min_samples`, and produces clusters along with the noise points (the points that could not be assigned to any particular cluster, indicated as -1). These tunable parameters directly affect the cluster results. We use greedy search over the hyperparameters and check what are the good candidates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eps:0.1\n",
      "eps:0.2\n",
      "eps:0.30000000000000004\n",
      "eps:0.4\n",
      "eps:0.5\n",
      "eps:0.6\n",
      "eps:0.7000000000000001\n",
      "eps:0.8\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_dt = utils.dbscan_hyperparameters(node_embeddings, e_lower=0.1, e_upper=0.9, m_lower=5, m_upper=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_clusters</th>\n",
       "      <th>n_noise</th>\n",
       "      <th>eps</th>\n",
       "      <th>min_samples</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>64</td>\n",
       "      <td>0.3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14</td>\n",
       "      <td>271</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>306</td>\n",
       "      <td>0.2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14</td>\n",
       "      <td>353</td>\n",
       "      <td>0.2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16</td>\n",
       "      <td>380</td>\n",
       "      <td>0.2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>410</td>\n",
       "      <td>0.2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>13</td>\n",
       "      <td>462</td>\n",
       "      <td>0.2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>13</td>\n",
       "      <td>496</td>\n",
       "      <td>0.2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>523</td>\n",
       "      <td>0.2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>15</td>\n",
       "      <td>570</td>\n",
       "      <td>0.2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13</td>\n",
       "      <td>629</td>\n",
       "      <td>0.2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47</td>\n",
       "      <td>1274</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1399</td>\n",
       "      <td>0.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>1535</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>1617</td>\n",
       "      <td>0.1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>1727</td>\n",
       "      <td>0.1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21</td>\n",
       "      <td>1796</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>22</td>\n",
       "      <td>1876</td>\n",
       "      <td>0.1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17</td>\n",
       "      <td>1965</td>\n",
       "      <td>0.1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14</td>\n",
       "      <td>2027</td>\n",
       "      <td>0.1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14</td>\n",
       "      <td>2083</td>\n",
       "      <td>0.1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n_clusters  n_noise  eps  min_samples\n",
       "20           2       49  0.3            5\n",
       "21           3       64  0.3            6\n",
       "22           2       76  0.3            7\n",
       "10          14      271  0.2            5\n",
       "11          15      306  0.2            6\n",
       "12          14      353  0.2            7\n",
       "13          16      380  0.2            8\n",
       "14          15      410  0.2            9\n",
       "15          13      462  0.2           10\n",
       "16          13      496  0.2           11\n",
       "17          17      523  0.2           12\n",
       "18          15      570  0.2           13\n",
       "19          13      629  0.2           14\n",
       "0           47     1274  0.1            5\n",
       "1           37     1399  0.1            6\n",
       "2           28     1535  0.1            7\n",
       "3           27     1617  0.1            8\n",
       "4           21     1727  0.1            9\n",
       "5           21     1796  0.1           10\n",
       "6           22     1876  0.1           11\n",
       "7           17     1965  0.1           12\n",
       "8           14     2027  0.1           13\n",
       "9           14     2083  0.1           14"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print results where there are more clusters than 1, and sort by the number of noise points\n",
    "db_dt.sort_values(by=['n_noise'])[db_dt.n_clusters > 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pick the hyperparameters, where the clustering results have as little noise points as possible, but also create number of clusters of reasonable size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# perform dbscan with the chosen parameters:\n",
    "db = DBSCAN(eps=0.1, min_samples=5).fit(node_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating the clustering statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated number of clusters: 47\n",
      "Estimated number of noise points: 1274\n",
      "Silhouette Coefficient: -0.098\n"
     ]
    }
   ],
   "source": [
    "labels = db.labels_\n",
    "# Number of clusters in labels, ignoring noise if present.\n",
    "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "n_noise_ = list(labels).count(-1)\n",
    "print('Estimated number of clusters: %d' % n_clusters_)\n",
    "print('Estimated number of noise points: %d' % n_noise_)\n",
    "print(\"Silhouette Coefficient: %0.3f\"\n",
    "      % metrics.silhouette_score(node_embeddings, labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot t-sne again but with the colours corresponding to dbscan points. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 't-sne with colors corresponding to dbscan cluster. Without noise points')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "emb_transformed['dbacan_clusters'] = labels\n",
    "X = emb_transformed[emb_transformed['dbacan_clusters'] != -1]\n",
    "\n",
    "\n",
    "plt.scatter(X[0], X[1], c=X['dbacan_clusters'], \n",
    "            cmap='Spectral', edgecolors='black', alpha=0.3, s=100) \n",
    "plt.title(\"t-sne with colors corresponding to dbscan cluster. Without noise points\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating GSEC and infomap qualitative differences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the resulting GSEC clusters, and explore, as an example, one particular cluster of a reasonable size, which is not a subset of any single infomap community. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display cluster sizes for first 15 largest clusters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "-1     1274\n",
       " 0      427\n",
       " 4      369\n",
       " 16     311\n",
       " 6      216\n",
       " 7      185\n",
       " 18     111\n",
       " 21      90\n",
       " 3       69\n",
       " 19      60\n",
       " 13      33\n",
       " 31      31\n",
       " 9       21\n",
       " 10      18\n",
       " 2       16\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustered_df = pd.DataFrame(node_embeddings, index=node_ids)\n",
    "clustered_df['cluster'] = db.labels_\n",
    "clustered_df.groupby('cluster').count()[0].sort_values(ascending=False)[0:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to display clusters that differ from infomap communities, as they are more interesting in this context. Therefore we calculate for each DBSCAN cluster how many different infomap communities it contains. The results are displayed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "-1     145\n",
       " 0      31\n",
       " 1       5\n",
       " 4      22\n",
       " 5       2\n",
       " 6       9\n",
       " 7      10\n",
       " 8       2\n",
       " 9       5\n",
       " 10      3\n",
       " 11      5\n",
       " 12      2\n",
       " 13      2\n",
       " 14      2\n",
       " 16      3\n",
       " 17      2\n",
       " 18      8\n",
       " 20      4\n",
       " 22      2\n",
       " 23      2\n",
       " 25      3\n",
       " 29      2\n",
       " 31      8\n",
       " 32      3\n",
       " 33      3\n",
       " 37      2\n",
       " 40      2\n",
       " 43      2\n",
       "Name: infomap, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inf_db_cm = clustered_df[['cluster']]\n",
    "inf_db_cm['infomap']= inf_db_cm.index.map(infomap_com_dict)\n",
    "dbscan_different = inf_db_cm.groupby('cluster')['infomap'].nunique() # if 1 all belong to same infomap cluster\n",
    "# show only those clusters that are not the same as infomap\n",
    "dbscan_different[dbscan_different != 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, DBSCAN `cluster_12` has nodes that were assigned to 2 different infomap clusters, while `cluster_31` has nodes from 8 different infomap communities.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Single cluster visualisation\n",
    "\n",
    "Now that we've selected a GSEC cluster (id=20) of reasonable size that contains nodes belonging to 4 different infomap communities, let's explore it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__To visualise a particular cluster, specify its number here:__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# specify the cluster id here:\n",
    "cluster_id = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Al-Qaida in Yemen',\n",
       " 'Harakat al-Nujaba',\n",
       " 'Al-Ashtar Brigades',\n",
       " 'Al-Haydariyah Battalion',\n",
       " 'February 14th Movement',\n",
       " 'Martyr al-Nimr Battalion',\n",
       " 'Popular Resistance Brigade',\n",
       " 'Saraya Waad Allah',\n",
       " 'Al-Qaida in Saudi Arabia',\n",
       " \"People's Defense Unit (Turkey)\",\n",
       " 'The Independent Military Wing of the Syrian Revolution Abroad']"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# manually look at the terrorist group names\n",
    "list(clustered_df.index[clustered_df.cluster==cluster_id])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a subgraph from the nodes in the cluster\n",
    "cluster_G = G.subgraph(list(clustered_df.index[clustered_df.cluster==cluster_id]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List for each of the `gname` (terrorist group name) in the cluster the assigned infomap community id. This shows whether the similar community was produced by infomap or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Al-Qaida in Yemen': 25,\n",
       " 'Harakat al-Nujaba': 9,\n",
       " 'Al-Ashtar Brigades': 96,\n",
       " 'Al-Haydariyah Battalion': 96,\n",
       " 'February 14th Movement': 96,\n",
       " 'Martyr al-Nimr Battalion': 96,\n",
       " 'Popular Resistance Brigade': 96,\n",
       " 'Saraya Waad Allah': 96,\n",
       " 'Al-Qaida in Saudi Arabia': 25,\n",
       " \"People's Defense Unit (Turkey)\": 31,\n",
       " 'The Independent Military Wing of the Syrian Revolution Abroad': 31}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison = {k : v for k,v in infomap_com_dict.items() if k in list(clustered_df.index[clustered_df.cluster==cluster_id])}\n",
    "comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another metric of clustering quality, we display how many edges are inside this cluster vs how many nodes are going outside (only one of edge endpoints is inside a cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "external_internal_edges = utils.cluster_external_internal_edges(G, inf_db_cm, 'cluster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster</th>\n",
       "      <th>nexternal_edges</th>\n",
       "      <th>ninternal_edges</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>20</td>\n",
       "      <td>107</td>\n",
       "      <td>16</td>\n",
       "      <td>0.149533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cluster  nexternal_edges  ninternal_edges     ratio\n",
       "23       20              107               16  0.149533"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "external_internal_edges[external_internal_edges.cluster==cluster_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the structure only\n",
    "pos = nx.fruchterman_reingold_layout(cluster_G, seed=123, iterations=30)\n",
    "plt.figure(figsize=(10, 8))\n",
    "nx.draw_networkx(cluster_G, pos, edge_color='#26282b', node_color='blue', alpha=0.3)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that terrorist groups (nodes) are connected, when at least one of the attacks was performed in the same decade in the same country. Therefore the connectivity indicates spatio-temporal similarity.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are quite a few clusters that are similar to infomap clusters. We pick a cluster that is not a subset of any single infomap community. We can see that there are disconnected groups of nodes in this cluster. \n",
    "\n",
    "So why are these disconnected components combined into one cluster? GraphSAGE embeddings directly depend on both node features and an underlying graph structure. Therefore, it makes sense to investigate similarity of features of the nodes in the cluster. It can highlight why these terrorist groups are combined together by GSEC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col3 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col4 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col5 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col6 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col7 {\n",
       "            background-color:  #199750;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col8 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col9 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col10 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col11 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col12 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col13 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col14 {\n",
       "            background-color:  #219c52;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col15 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col16 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col17 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col18 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col0 {\n",
       "            background-color:  #fece7c;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col1 {\n",
       "            background-color:  #fdbf6f;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col2 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col3 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col4 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col5 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col6 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col7 {\n",
       "            background-color:  #fee08b;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col8 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col9 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col10 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col11 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col12 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col13 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col14 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col15 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col16 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col17 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col18 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col0 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col1 {\n",
       "            background-color:  #15904c;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col2 {\n",
       "            background-color:  #fdbd6d;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col3 {\n",
       "            background-color:  #3ca959;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col4 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col5 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col6 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col7 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col8 {\n",
       "            background-color:  #87cb67;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col9 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col10 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col11 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col12 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col13 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col14 {\n",
       "            background-color:  #b7e075;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col15 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col16 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col17 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col18 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col0 {\n",
       "            background-color:  #c7e77f;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col1 {\n",
       "            background-color:  #b7e075;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col2 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col3 {\n",
       "            background-color:  #04703b;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col4 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col5 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col6 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col7 {\n",
       "            background-color:  #fffebe;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col8 {\n",
       "            background-color:  #87cb67;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col9 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col10 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col11 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col12 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col13 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col14 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col15 {\n",
       "            background-color:  #b7e075;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col16 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col17 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col18 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col0 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col1 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col2 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col3 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col4 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col5 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col6 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col7 {\n",
       "            background-color:  #199750;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col8 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col9 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col10 {\n",
       "            background-color:  #b7e075;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col11 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col12 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col13 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col14 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col15 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col16 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col17 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col18 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col0 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col1 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col2 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col3 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col4 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col5 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col6 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col7 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col8 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col9 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col10 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col11 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col12 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col13 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col14 {\n",
       "            background-color:  #219c52;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col15 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col16 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col17 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col18 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col0 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col1 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col2 {\n",
       "            background-color:  #036e3a;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col3 {\n",
       "            background-color:  #036e3a;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col4 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col5 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col6 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col7 {\n",
       "            background-color:  #199750;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col8 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col9 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col10 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col11 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col12 {\n",
       "            background-color:  #a50026;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col13 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col14 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col15 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col16 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col17 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col18 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col0 {\n",
       "            background-color:  #17934e;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col1 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col2 {\n",
       "            background-color:  #036e3a;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col3 {\n",
       "            background-color:  #04703b;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col4 {\n",
       "            background-color:  #fffebe;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col5 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col6 {\n",
       "            background-color:  #006837;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col7 {\n",
       "            background-color:  #66bd63;\n",
       "        }    #T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col8 {\n",
       "            background-color:  #006837;\n",
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       "        }</style>  \n",
       "<table id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >n_attacks</th> \n",
       "        <th class=\"col_heading level0 col1\" >n_nperp</th> \n",
       "        <th class=\"col_heading level0 col2\" >n_nkil</th> \n",
       "        <th class=\"col_heading level0 col3\" >n_nwound</th> \n",
       "        <th class=\"col_heading level0 col4\" >success_ratio</th> \n",
       "        <th class=\"col_heading level0 col5\" >Armed Assault</th> \n",
       "        <th class=\"col_heading level0 col6\" >Assassination</th> \n",
       "        <th class=\"col_heading level0 col7\" >Bombing/Explosion</th> \n",
       "        <th class=\"col_heading level0 col8\" >Business</th> \n",
       "        <th class=\"col_heading level0 col9\" >Educational Institution</th> \n",
       "        <th class=\"col_heading level0 col10\" >Government (Diplomatic)</th> \n",
       "        <th class=\"col_heading level0 col11\" >Government (General)</th> \n",
       "        <th class=\"col_heading level0 col12\" >Journalists & Media</th> \n",
       "        <th class=\"col_heading level0 col13\" >Military</th> \n",
       "        <th class=\"col_heading level0 col14\" >Police</th> \n",
       "        <th class=\"col_heading level0 col15\" >Private Citizens & Property</th> \n",
       "        <th class=\"col_heading level0 col16\" >Religious Figures/Institutions</th> \n",
       "        <th class=\"col_heading level0 col17\" >Tourists</th> \n",
       "        <th class=\"col_heading level0 col18\" >Utilities</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >gname</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1level0_row0\" class=\"row_heading level0 row0\" >Al-Ashtar Brigades</th> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col0\" class=\"data row0 col0\" >12</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col1\" class=\"data row0 col1\" >12</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col2\" class=\"data row0 col2\" >6</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col3\" class=\"data row0 col3\" >24</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col4\" class=\"data row0 col4\" >0.916667</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col5\" class=\"data row0 col5\" >2</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col6\" class=\"data row0 col6\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col7\" class=\"data row0 col7\" >10</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col8\" class=\"data row0 col8\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col9\" class=\"data row0 col9\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col10\" class=\"data row0 col10\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col11\" class=\"data row0 col11\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col12\" class=\"data row0 col12\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col13\" class=\"data row0 col13\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col14\" class=\"data row0 col14\" >9</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col15\" class=\"data row0 col15\" >2</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col16\" class=\"data row0 col16\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col17\" class=\"data row0 col17\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row0_col18\" class=\"data row0 col18\" >0</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1level0_row1\" class=\"row_heading level0 row1\" >Al-Haydariyah Battalion</th> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col0\" class=\"data row1 col0\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col1\" class=\"data row1 col1\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col2\" class=\"data row1 col2\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col3\" class=\"data row1 col3\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col4\" class=\"data row1 col4\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col5\" class=\"data row1 col5\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col6\" class=\"data row1 col6\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col7\" class=\"data row1 col7\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col8\" class=\"data row1 col8\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col9\" class=\"data row1 col9\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col10\" class=\"data row1 col10\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col11\" class=\"data row1 col11\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col12\" class=\"data row1 col12\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col13\" class=\"data row1 col13\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col14\" class=\"data row1 col14\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col15\" class=\"data row1 col15\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col16\" class=\"data row1 col16\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col17\" class=\"data row1 col17\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row1_col18\" class=\"data row1 col18\" >0</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1level0_row2\" class=\"row_heading level0 row2\" >Al-Qaida in Saudi Arabia</th> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col0\" class=\"data row2 col0\" >8</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col1\" class=\"data row2 col1\" >8</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col2\" class=\"data row2 col2\" >73</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col3\" class=\"data row2 col3\" >241</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col4\" class=\"data row2 col4\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col5\" class=\"data row2 col5\" >2</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col6\" class=\"data row2 col6\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col7\" class=\"data row2 col7\" >6</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col8\" class=\"data row2 col8\" >4</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col9\" class=\"data row2 col9\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col10\" class=\"data row2 col10\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col11\" class=\"data row2 col11\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col12\" class=\"data row2 col12\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col13\" class=\"data row2 col13\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col14\" class=\"data row2 col14\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col15\" class=\"data row2 col15\" >3</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col16\" class=\"data row2 col16\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col17\" class=\"data row2 col17\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row2_col18\" class=\"data row2 col18\" >0</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1level0_row3\" class=\"row_heading level0 row3\" >Al-Qaida in Yemen</th> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col0\" class=\"data row3 col0\" >12</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col1\" class=\"data row3 col1\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col2\" class=\"data row3 col2\" >49</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col3\" class=\"data row3 col3\" >35</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col4\" class=\"data row3 col4\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col5\" class=\"data row3 col5\" >2</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col6\" class=\"data row3 col6\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col7\" class=\"data row3 col7\" >10</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col8\" class=\"data row3 col8\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col9\" class=\"data row3 col9\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col10\" class=\"data row3 col10\" >3</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col11\" class=\"data row3 col11\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col12\" class=\"data row3 col12\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col13\" class=\"data row3 col13\" >2</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col14\" class=\"data row3 col14\" >3</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col15\" class=\"data row3 col15\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col16\" class=\"data row3 col16\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col17\" class=\"data row3 col17\" >2</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row3_col18\" class=\"data row3 col18\" >1</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1level0_row4\" class=\"row_heading level0 row4\" >February 14th Movement</th> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col0\" class=\"data row4 col0\" >5</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col1\" class=\"data row4 col1\" >4</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col2\" class=\"data row4 col2\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col3\" class=\"data row4 col3\" >4</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col4\" class=\"data row4 col4\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col5\" class=\"data row4 col5\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col6\" class=\"data row4 col6\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col7\" class=\"data row4 col7\" >5</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col8\" class=\"data row4 col8\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col9\" class=\"data row4 col9\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col10\" class=\"data row4 col10\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col11\" class=\"data row4 col11\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col12\" class=\"data row4 col12\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col13\" class=\"data row4 col13\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col14\" class=\"data row4 col14\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col15\" class=\"data row4 col15\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col16\" class=\"data row4 col16\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col17\" class=\"data row4 col17\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row4_col18\" class=\"data row4 col18\" >1</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1level0_row5\" class=\"row_heading level0 row5\" >Harakat al-Nujaba</th> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col0\" class=\"data row5 col0\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col1\" class=\"data row5 col1\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col2\" class=\"data row5 col2\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col3\" class=\"data row5 col3\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col4\" class=\"data row5 col4\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col5\" class=\"data row5 col5\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col6\" class=\"data row5 col6\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col7\" class=\"data row5 col7\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col8\" class=\"data row5 col8\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col9\" class=\"data row5 col9\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col10\" class=\"data row5 col10\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col11\" class=\"data row5 col11\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col12\" class=\"data row5 col12\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col13\" class=\"data row5 col13\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col14\" class=\"data row5 col14\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col15\" class=\"data row5 col15\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col16\" class=\"data row5 col16\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col17\" class=\"data row5 col17\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row5_col18\" class=\"data row5 col18\" >0</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1level0_row6\" class=\"row_heading level0 row6\" >Martyr al-Nimr Battalion</th> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col0\" class=\"data row6 col0\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col1\" class=\"data row6 col1\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col2\" class=\"data row6 col2\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col3\" class=\"data row6 col3\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col4\" class=\"data row6 col4\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col5\" class=\"data row6 col5\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col6\" class=\"data row6 col6\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col7\" class=\"data row6 col7\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col8\" class=\"data row6 col8\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col9\" class=\"data row6 col9\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col10\" class=\"data row6 col10\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col11\" class=\"data row6 col11\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col12\" class=\"data row6 col12\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col13\" class=\"data row6 col13\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col14\" class=\"data row6 col14\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col15\" class=\"data row6 col15\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col16\" class=\"data row6 col16\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col17\" class=\"data row6 col17\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row6_col18\" class=\"data row6 col18\" >0</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1level0_row7\" class=\"row_heading level0 row7\" >People's Defense Unit (Turkey)</th> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col0\" class=\"data row7 col0\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col1\" class=\"data row7 col1\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col2\" class=\"data row7 col2\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col3\" class=\"data row7 col3\" >3</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col4\" class=\"data row7 col4\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col5\" class=\"data row7 col5\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col6\" class=\"data row7 col6\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col7\" class=\"data row7 col7\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col8\" class=\"data row7 col8\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col9\" class=\"data row7 col9\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col10\" class=\"data row7 col10\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col11\" class=\"data row7 col11\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col12\" class=\"data row7 col12\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col13\" class=\"data row7 col13\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col14\" class=\"data row7 col14\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col15\" class=\"data row7 col15\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col16\" class=\"data row7 col16\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col17\" class=\"data row7 col17\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row7_col18\" class=\"data row7 col18\" >0</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1level0_row8\" class=\"row_heading level0 row8\" >Popular Resistance Brigade</th> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col0\" class=\"data row8 col0\" >2</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col1\" class=\"data row8 col1\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col2\" class=\"data row8 col2\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col3\" class=\"data row8 col3\" >4</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col4\" class=\"data row8 col4\" >0.5</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col5\" class=\"data row8 col5\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col6\" class=\"data row8 col6\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col7\" class=\"data row8 col7\" >2</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col8\" class=\"data row8 col8\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col9\" class=\"data row8 col9\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col10\" class=\"data row8 col10\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col11\" class=\"data row8 col11\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col12\" class=\"data row8 col12\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col13\" class=\"data row8 col13\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col14\" class=\"data row8 col14\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col15\" class=\"data row8 col15\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col16\" class=\"data row8 col16\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col17\" class=\"data row8 col17\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row8_col18\" class=\"data row8 col18\" >0</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1level0_row9\" class=\"row_heading level0 row9\" >Saraya Waad Allah</th> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col0\" class=\"data row9 col0\" >3</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col1\" class=\"data row9 col1\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col2\" class=\"data row9 col2\" >3</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col3\" class=\"data row9 col3\" >20</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col4\" class=\"data row9 col4\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col5\" class=\"data row9 col5\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col6\" class=\"data row9 col6\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col7\" class=\"data row9 col7\" >3</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col8\" class=\"data row9 col8\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col9\" class=\"data row9 col9\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col10\" class=\"data row9 col10\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col11\" class=\"data row9 col11\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col12\" class=\"data row9 col12\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col13\" class=\"data row9 col13\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col14\" class=\"data row9 col14\" >3</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col15\" class=\"data row9 col15\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col16\" class=\"data row9 col16\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col17\" class=\"data row9 col17\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row9_col18\" class=\"data row9 col18\" >0</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1level0_row10\" class=\"row_heading level0 row10\" >The Independent Military Wing of the Syrian Revolution Abroad</th> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col0\" class=\"data row10 col0\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col1\" class=\"data row10 col1\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col2\" class=\"data row10 col2\" >2</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col3\" class=\"data row10 col3\" >3</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col4\" class=\"data row10 col4\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col5\" class=\"data row10 col5\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col6\" class=\"data row10 col6\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col7\" class=\"data row10 col7\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col8\" class=\"data row10 col8\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col9\" class=\"data row10 col9\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col10\" class=\"data row10 col10\" >1</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col11\" class=\"data row10 col11\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col12\" class=\"data row10 col12\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col13\" class=\"data row10 col13\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col14\" class=\"data row10 col14\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col15\" class=\"data row10 col15\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col16\" class=\"data row10 col16\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col17\" class=\"data row10 col17\" >0</td> \n",
       "        <td id=\"T_c7908192_43dd_11e9_9b75_8c85905141a1row10_col18\" class=\"data row10 col18\" >0</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a24eeaef0>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_feats = filtered_features[filtered_features.index.isin(list(clustered_df.index[clustered_df.cluster==cluster_id]))]\n",
    "# show only non-zero columns\n",
    "features_nonzero = cluster_feats.loc[:, (cluster_feats != 0).any()]\n",
    "features_nonzero.style.background_gradient(cmap='RdYlGn_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most of the isolated nodes in the cluster have features similar to those in the tight clique, e.g., in most cases they have high number of attacks, high success ratio, and attacks being focused mostly on bombings, and their targets are often the police. \n",
    "\n",
    "Note that there are terrorist groups that differ from the rest of the groups in the cluster in terms of their features. By taking a closer look we can observe that these terrorist groups are a part of a tight clique. For example, _Martyr al-Nimr Battalion_ has number of bombings equal to 0, but it is a part of a fully connected subgraph. \n",
    "\n",
    "Interestingly, _Al-Qaida in Saudi Arabia_ ends up in the same cluster as _Al-Qaida in Yemen_, though they are not connected directly in the network.    \n",
    "\n",
    "Thus we can observe that clustering on GraphSAGE embeddings combines groups based both on the underlying structure as well as features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
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if (gjData.features.length != 0) {
  var gj = L.geoJson(gjData, {
    style: function (feature) {
      return feature.properties;
    },
    pointToLayer: function (feature, latlng) {
      var icon = L.divIcon({'html': feature.properties.html, 
        iconAnchor: [feature.properties.anchor_x, 
                     feature.properties.anchor_y], 
          className: 'empty'});  // What can I do about empty?
      return L.marker(latlng, {icon: icon});
    }
  });
  gj.addTo(map);
  
  map.fitBounds(gj.getBounds());
} else {
  map.setView([0, 0], 1);
}
</script>
</body>\" width=\"100%\" height=\"720\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes = cluster_G.nodes()\n",
    "com_avg_coords = avg_coords[avg_coords.index.isin(list(nodes))]\n",
    "new_order = [1,0]\n",
    "com_avg_coords = com_avg_coords[com_avg_coords.columns[new_order]]\n",
    "com_avg_coords.fillna(com_avg_coords.mean(), inplace=True) # fill missing values with the average\n",
    "pos = com_avg_coords.T.to_dict('list')  # layout is based on the provided coordindates\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(22,12))\n",
    "\n",
    "nx.draw_networkx_nodes(cluster_G, pos, nodelist=nodes, \n",
    "                            with_labels=True, node_size=200, alpha=0.5)\n",
    "nx.draw_networkx_labels(cluster_G, pos, font_color='red', font_size=50)\n",
    "nx.draw_networkx_edges(cluster_G, pos, edge_color='grey')\n",
    "\n",
    "mplleaflet.display(fig=ax.figure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(**N.B.:** the above interactive plot will only appear after running the cell, and is not rendered in Github!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These groups are also very closely located, but in contrast with infomap, this is not the only thing that defines the clustering groups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GSEC results\n",
    "\n",
    "What we can see from the results above is a somehow different picture from the infomap (note that by rerunning this notebook, the results might change due to the nature of the GraphSAGE predictions). Some clusters are identical to the infomap, showing that GSEC __capture__ the network structure. But some of the clusters differ - they are not necessary connected. By observing the feature table we can see that it has terrorist groups with __similar characteristics__. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this use case we demonstrated the conceptual differences of traditional community detection and unsupervised GraphSAGE embeddings clustering (GSEC) on a real dataset. We can observe that the traditional approach to the community detection via graph partitioning produces communities that are related to the graph structure only, while GSEC combines the features and the structure. However, in the latter case we should be very conscious of the network schema and the features, as these significantly affect both the clustering and the interpretation of the resulting clusters. \n",
    "\n",
    "For example, if we don't want the clusters where neither number of kills nor country play a role, we might want to exclude `nkills` from the feature set, and create a graph, where groups are connected only via the decade, and not via country. Then, the resulting groups would probably be related to the terrorist activities in time, and grouped by their similarity in their target and the types of attacks.  "
   ]
  }
 ],
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